Learning to negotiate optimally in non-stationary environments.
Narayanan, V. and Jennings, N. R. (2006) Learning to negotiate optimally in non-stationary environments. In, 10th International Workshop on Cooperative Information Agents, Edinburgh, U.K., , 288-300.
We Adopt the Markov chain framework to model bilateral negotiations among agents in dynamic environments and use Bayesian learning to enable them to learn an optimal strategy in incomplete information settings. Specifically, an agent learns the optimal strategy to play against an opponent whose strategy varies with time, assuming no prior information about its negotiation parameters. In doing so, we present a new framework for adaptive negotiation in such non-stationary environments and develop a novel learning algorithm, which is guaranteed to converge, that an agent can use to negotiate optimally over time. We have implemented our algorithm and shown that it converges quickly in a wide range of cases.
|Item Type:||Conference or Workshop Item (Paper)|
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Agents, Interactions & Complexity
|Date Deposited:||09 Oct 2006|
|Last Modified:||01 Mar 2012 22:07|
|Contributors:||Narayanan, V. (Author)
Jennings, N. R. (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||2|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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